Processing data to establish lifecycle threads in the development of a structural product

ABSTRACT

A method is provided for processing data to establish lifecycle threads in the development of a structural product. The method includes defining a source lifecycle thread from process-related information for development of the structural product, and defining and matching a target lifecycle thread to the source lifecycle thread. The process, and source and target lifecycle threads are expressible as respectively a network and sub-networks of tasks described by a plurality of attributes. For defining either or each of the source lifecycle thread or target lifecycle thread, the method includes at least receiving user selection of an attribute of the plurality of attributes as user-selected criteria, performing a cluster analysis according to the user-selected criteria to produce a plurality of clusters of candidate tasks from the plurality of tasks, and selecting tasks from the plurality of clusters of candidate tasks for the source/target lifecycle thread.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is related to U.S. patent application Ser. No.15/171,744, entitled: Processing Data to Replicate Lifecycle Threads inthe Development of a Structural Product, filed Jun. 2, 2016, the contentof which is incorporated herein by reference in its entirety.

TECHNOLOGICAL FIELD

The present disclosure relates generally to development of a structuralproduct and, in particular, to processing data to establish andreplicate lifecycle threads in the development of a structural productsuch as an aircraft.

BACKGROUND

Complex projects such as the planning and production of large commercialor military aircraft require the scheduling and coordination of aplurality of resources. The resources to be coordinated may includematerials, component parts, personnel, machinery and factory floorspace, in addition to other resources. Integration and coordination isparticularly important in complex projects since higher-order effectsand interactions can adversely affect the cost of the project, the timerequired for completion of the project, and the risk of failure todeliver the required content. In addition, other variables of importancesuch as the overall effectiveness of the project need to be modeled andmeasured.

For large scale, complex product development, the coordination ofresources is accomplished by modeling the steps in product developmentas tasks, with inputs associated to each task. The set of tasks, taskattributes, and the input relationships constitutes a model for thedevelopment and production of a product of interest. The inputs to eachtask may be produced by earlier tasks, or may be produced outside thebounds of the model and considered as external inputs.

The set of tasks developed for the coordination of resources are profuse(10,000-100,000 tasks) and often inconsistent in their naming conventionfor tasks and associated data names. Tasks are associated withrespective attributes. The profuse, inconsistent data and plurality ofattributes, along with the requirement to maintain a well-orderedprocess present obstacles to the use of emergent patterns as templatesto reduce the time and cost needed to develop project plans sufficientfor effective scheduling and coordination of resources.

These emergent patterns of tasks and inputs may be referred to as“threaded lifecycle patterns” or “lifecycle threads” when considered inthe lifecycle maturity phases of product development, such as acomponent part, assembly, subsystem or system. In order to detect thesethreaded lifecycle patterns in a network of tasks, it is necessary toassociate together a subset of these tasks that are related in anetwork. Techniques exist for finding patterns in data, but thesetechniques suffer from a number of drawbacks when considered forestablishment and use of threaded lifecycle patterns.

Therefore it would be desirable to have a system and method that takesinto account at least some of the issues discussed above, as well asother possible issues.

BRIEF SUMMARY

Clustering is a well-known technique for finding patterns in data.Cluster analysis or “clustering” is the task of grouping a set ofobjects in such a way that objects in the same group (called a cluster)are more similar (in some sense or another) to each other than to thosein other groups (clusters). Cluster analysis is not an automatic task,but an iterative process of knowledge discovery or interactivemulti-objective optimization that involves trial and failure. However,the existing solutions to clustering use standardized criteria andweights. The drawback is that the clusters are not customizable tocollect necessary network elements for a lifecycle thread.

A related obstacle is that the naming convention for tasks andassociated task data is not always consistent, rendering searchtechniques labor-intensive. For example, a current commercial airplaneprocess model in development has over 10,000 tasks and over 46,000 taskrelationships. When complete, this model will have over 100,000 tasksand 500,000 task relationships. The obstacle with interactive handlingof large data sets using current approaches is that largemulti-dimensional matrices are required. Due to the processingrequirements for these large multi-dimensional matrices, interactiveinterrogation of the data is infeasible. With N tasks, on the order ofN² comparisons are needed. Using conventional methods, each comparisonwould require a distance computation involving in an exampleimplementation up to eight attributes.

Example implementations of the present disclosure are generally directedto a system, and corresponding method and computer-readable storagemedium for processing data to establish and replicate lifecycle threadsin the development of one or more structural products such as aircraft.Example implementations enable customization of clusters to collectnecessary network elements for a lifecycle thread. Exampleimplementations also replace computationally intensive operations withlookup and addition to reduce the computation, and used distance maps toreduce data storage requirements.

Example implementations of the present disclosure introduces the conceptof a manifest that contains and maintains grouping information and thecurrent clustering state. The grouping construct may be instantiated foreach attribute (data field) selected for clustering, and the clusteringstate is the association of tasks with clusters. Each grouping maintainsa mapping between the unique values of an attribute and the distancesbetween those values, as defined by a distance metric for the attribute.In some examples, the unique value distance mapping of the groupingmaintains distances as normalized quadrance components for eachattribute. By maintaining a mapping of unique values and the distancesbetween them as quadrance components, interactive processing is reducedto an ordinal comparison. This avoids the computational-intensive squareroot, and many multiplication and addition steps.

The present disclosure thus includes, without limitation, the followingexample implementations. In some example implementations, a method isprovided for processing data to establish lifecycle threads in thedevelopment of a structural product. The method includes accessing, byprocessing circuitry, a memory storing process-related informationdescribing at least one hierarchical, well-ordered process expressibleas at least one network including a plurality of tasks to produceinternal products corresponding to components of at least one structuralproduct produced thereby, at least some of the plurality of tasks havinga precedence relationship whereby the internal product produced by onetask is an input to another task; defining, by the processing circuitry,a source lifecycle thread from the process-related information; anddefining and matching, by the processing circuitry, a target lifecyclethread to the source lifecycle thread, the source lifecycle thread andtarget lifecycle thread each being expressible as a sub-network ofrespectively source tasks and target tasks selected from the pluralityof tasks, the target tasks having at least a threshold similarity withthe source tasks, and inputs to the target tasks having at least athreshold similarity with inputs to the source tasks, wherein theprocess-related information describes the plurality of tasks by aplurality of attributes

In the foregoing example implementations, either or each of defining thesource lifecycle thread, or defining and matching the target lifecyclethread, includes at least receiving user selection of an attribute ofthe plurality of attributes as user-selected criteria; performing acluster analysis according to the user-selected criteria to produce aplurality of clusters of candidate tasks from the plurality of tasks,performing the cluster analysis including forming a grouping of theplurality of tasks according to the attribute, generating a distance mapfor the grouping including only unique values of the attribute anddistances between the unique values, and organizing the tasks into theplurality of clusters of candidate tasks according to distance using thedistance map; and selecting tasks from the plurality of clusters ofcandidate tasks for the source tasks or target tasks.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, receivinguser selection of the attribute includes receiving user selection of aplurality of attributes as the user-selected criteria, wherein formingthe grouping includes forming a respective plurality of groupingsaccording to the plurality of attributes, generating the distance mapincludes generating a distance map for each grouping of the respectiveplurality of groupings, and organizing the tasks includes organizing thetasks using the distance map for each grouping, and wherein selectingthe tasks includes selecting the tasks from a selected cluster of theplurality of clusters.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, receivinguser selection of the plurality of attributes includes receiving userselection of the plurality of attributes and a weight for a particularattribute thereof as the user-selected criteria, and wherein performingthe cluster analysis further includes applying the weight to thedistance map for the grouping according to the particular attribute, andorganizing the tasks includes organizing the tasks using the distancemap for each grouping including the distance map with the weight soapplied.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, definingthe source lifecycle thread or defining the target lifecycle threadfurther includes receiving user selection of an adjusted weight for theparticular attribute, and wherein performing the cluster analysisfurther includes applying the adjusted weight to the distance map forthe grouping according to the particular attribute, and reorganizing thetasks using the distance map for each grouping including the distancemap with the adjusted weight so applied.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, each ofdefining the source lifecycle thread, and defining and matching thetarget lifecycle thread, includes receiving user selection of theattribute, performing the cluster analysis and selecting the tasks, andwherein in an instance in which the attribute received for both thesource lifecycle thread and target lifecycle thread is the same,defining and matching the target lifecycle thread includes organizingthe tasks using the distance map generated during the cluster analysisfor the source lifecycle thread without again generating the distancemap during the cluster analysis for the target lifecycle thread.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, definingand matching the target lifecycle thread includes at least selecting aplurality of candidate target tasks from the plurality of tasks, theplurality of candidate target tasks being selected from the plurality ofclusters of candidate tasks in an instance in which defining andmatching the target lifecycle thread includes performing the clusteranalysis; matching a candidate target task to a particular source taskof the source lifecycle thread; and back-chaining through the sourcelifecycle thread including for each source task of at least some of thesource tasks beginning with the particular source task: identifying apreceding source task that inputs to the source task; and matchinganother candidate target task to any preceding source task, the targetlifecycle thread being composed of the candidate target task and othercandidate target task matched to the preceding source task for eachsource task of the at least some of the source tasks.

In some example implementations of the method of any preceding or anysubsequent example implementation, or any combination thereof, in atleast one instance in which the candidate target task and othercandidate target task are precedence unrelated, back-chaining throughthe source lifecycle thread further includes defining an input to thecandidate target task from the other candidate target task to therebyestablish a precedence relationship.

In some example implementations, an apparatus is provided for processingdata to establish lifecycle threads in the development of a structuralproduct. The apparatus comprises a memory storing process-relatedinformation, and processing circuitry configured to access the memory,and programmed to at least perform the method of any preceding exampleimplementation, or any combination thereof. This may includeimplementation of a lifecycle thread establishment and replicationsystem including a source lifecycle thread subsystem and a targetlifecycle thread subsystem coupled to one another and configured toperform steps of the method.

In some example implementations, a computer-readable storage medium isprovided for processing data to establish and replicate lifecyclethreads in the development of a structural product. Thecomputer-readable storage medium is non-transitory and hascomputer-readable program code portions stored therein that, in responseto execution by processing circuitry, cause an apparatus to at leastperform the method of any preceding example implementation, or anycombination thereof.

These and other features, aspects, and advantages of the presentdisclosure will be apparent from a reading of the following detaileddescription together with the accompanying drawings, which are brieflydescribed below. The present disclosure includes any combination of two,three, four or more features or elements set forth in this disclosure,regardless of whether such features or elements are expressly combinedor otherwise recited in a specific example implementation describedherein. This disclosure is intended to be read holistically such thatany separable features or elements of the disclosure, in any of itsaspects and example implementations, should be viewed as intended,namely to be combinable, unless the context of the disclosure clearlydictates otherwise.

It will therefore be appreciated that this Brief Summary is providedmerely for purposes of summarizing some example implementations so as toprovide a basic understanding of some aspects of the disclosure.Accordingly, it will be appreciated that the above described exampleimplementations are merely examples and should not be construed tonarrow the scope or spirit of the disclosure in any way. Other exampleimplementations, aspects and advantages will become apparent from thefollowing detailed description taken in conjunction with theaccompanying drawings which illustrate, by way of example, theprinciples of some described example implementations.

BRIEF DESCRIPTION OF THE DRAWING(S)

Having thus described example implementations of the disclosure ingeneral terms, reference will now be made to the accompanying drawings,which are not necessarily drawn to scale, and wherein:

FIGS. 1 and 2 illustrate feed-forward networks in accordance withexample implementations of the present disclosure;

FIG. 3 illustrates a network diagram of one example of a suitablelifecycle thread according to example implementations;

FIG. 4 illustrates a lifecycle thread establishment and replicationsystem according to example implementations;

FIG. 5 illustrates one example of a suitable clustering subsystemaccording to example implementations;

FIG. 6 illustrates one example of a suitable cluster analysis engineaccording to example implementations;

FIG. 7 illustrates a target lifecycle thread subsystem according toexample implementations;

FIGS. 8A and 8B are flowcharts illustrating various steps in a methodaccording to various example implementations;

FIGS. 9A and 9B are flowcharts illustrating various steps in a methodaccording to other example implementations;

FIGS. 10, 11 and 12 are flowcharts illustrating operations in routinesor subroutines that may be performed according to exampleimplementations; and

FIGS. 13, 14, 15, 16 and 17 illustrate displays of a graphical userinterface (GUI) according to example implementations; and

FIG. 18 illustrates an apparatus according to some exampleimplementations.

DETAILED DESCRIPTION

Some implementations of the present disclosure will now be describedmore fully hereinafter with reference to the accompanying drawings, inwhich some, but not all implementations of the disclosure are shown.Indeed, various implementations of the disclosure may be embodied inmany different forms and should not be construed as limited to theimplementations set forth herein; rather, these example implementationsare provided so that this disclosure will be thorough and complete, andwill fully convey the scope of the disclosure to those skilled in theart. For example, unless otherwise indicated, reference something asbeing a first, second or the like should not be construed to imply aparticular order. Also, something may be described as being abovesomething else (unless otherwise indicated) may instead be below, andvice versa; and similarly, something described as being to the left ofsomething else may instead be to the right, and vice versa. Likereference numerals refer to like elements throughout.

Example implementations of the present disclosure are directed toestablishment and replication of lifecycle threads in one or moreprocesses for production of one or more products—i.e.,product-production process(es). In accordance with exampleimplementations, a product, including physical products (e.g., objects)and non-physical products (e.g., information), may be described in termsof a hierarchical breakdown (hereinafter referred to as a “hierarchy”)of the product's components. A “process” may be adapted to describeproduction of the product by defining tasks and precedences associatedwith the creation of each component. For example, the precedences mayindicate that a particular task should be completed before another taskis performed. In various examples, a task may refer to an activity orset of activities performed during creation of a component.

A complex process may include one or more sub-processes each of whichmay at times be considered a separate process without regard to thecomplex process or others of the sub-processes. In one example, theprocess may be expressible as a network. A suitable network may beconstructed in any of a number of different manners. In one example, thenetwork may be constructed in accordance with technologies described inU.S. Pat. No. 7,899,768 entitled: Methods and Systems for Constructing aScalable Hierarchical Feed-Forward Model for Fabricating a Product,issued Mar. 1, 2011, and U.S. Patent Application Publication No.2014/0222487, entitled: Total-Ordering in Process Planning, publishedAug. 7, 2014, the contents of both of which are hereby incorporated byreference in their respective entireties.

In some examples, the process, and thus its network expression, may beadapted to maintain a feed-forward constraint such that no cycles orloops are contained within the process. In some examples, the processand its network expression may also be adapted to maintain awell-ordered constraint such that its tasks are well-ordered (referringto a set in which every non-empty subset has a minimum element). Theprocess and its network expression may also be scalable such that it maybe combined with other processes to generate a larger process.

As used herein, a “product” may refer to something input into orproduced by a process in the network. An illustrative process may be acommercial aircraft development process. In one example, a product ofthe commercial aircraft development process may include an aircraft or apart of the aircraft (e.g., fuselage section, wing, landing gear,engine, etc.). In another example, the product may include a typecertificate or other relevant document related to legal use of theaircraft. In yet another example, the product may include a designspecification or other dataset related to the design and/or constructionof the aircraft. Some other examples of products may include a wingcenter section, a control column and wheel, an overhead stowage bin, alayout of passenger arrangement, front spar interface loads, pitchingmoment curves and the like.

The product may be either an “internal product” or an “externalproduct.” An internal product may be producible by one or more tasks inthe network (the respective one or more tasks in various instances beingconsidered a sub-process). In one example, an internal product may beconsidered a segment, which may be an internal product that is not acomponent of another internal product, but is instead intended to bebroken into more detailed components. An internal product may receive asinput an external product and/or an internal product. Some examples ofinternal products in the commercial aircraft development process mayinclude avionics, propulsion systems, engine-specific fuel consumptioncurves and the like. Each internal product may include one or more“internal inputs,” which may be utilized or needed to produce theinternal product.

The internal inputs may include “internal components” and “componentinputs.” The internal components may refer to a subset of non-externalinputs that is not part of the same segment as the internal product. Thecomponent inputs may refer to a subset of non-external inputs that ispart of the same segment as the internal product. Each component inputmay include multiple “component products,” the aggregate of which formthe component input. An illustrative internal product may be asubassembly. For the subassembly, an example component input may beparts of the subassembly, and an example internal component may be atool that assembles the parts to produce the subassembly. In this case,the parts assemble to form the subassembly. As such, the parts areconsidered in the same segment as the subassembly. In contrast, the toolthat assembles the parts is not included within the subassembly. Assuch, the tool is considered as not part of the same segment as thesubassembly.

The external product may be produced outside of a process in thenetwork. In contrast to the internal product, input to the externalproduct may not be represented in the context of the process or itsnetwork expression. Some examples of external products in the commercialaircraft development process may include regulatory requirements,customer requirements, company ground rules, existing facilities and thelike. The external product may include multiple components, theaggregate of which forms the external product. Each such componentforming the external product may be referred to herein as an “externalcomponent.” The internal products, external products, internalcomponents, component inputs and/or external components may form the setof inputs into a process adapted to produce any given internal product.

Each internal product may be a component. Each component may includemultiple nested components, and may further include additional nestedcomponents at deeper levels of the hierarchy. In the commercial aircraftdevelopment process, some examples of segment components may includetechnology assessment, exploratory design, conceptual design,preliminary design, production system, infrastructure, detailmanufacturing plans, vehicle product, product validation and the like.The example component “infrastructure” may include a nested component“production facilities,” which further includes a nested component“major assemblies.” The component “major assemblies” may include anested component “wing center section,” which further includes a nestedcomponent “upper panel.” Additional nested components may continue fromthe component “upper panel.”

As used herein, an “input” may refer to a product, such as an internalor external product, that may be utilized or required by the task toproduce another product. That is, a statement that a first product isinput to a second product may refer to the utilization or requirement ofthe first product by the task to produce the second product. Forexample, an internal product may be a design specification of anairplane wing. An external product may be specifications of fastenersthat are utilized or required in the production of the detailed design.In this case, since the design specification of the airplane wingutilizes or requires the specifications of fasteners, the specificationsof fasteners may also be referred to as an external input to the designspecification of the airplane wing. According to some exampleimplementations, an internal product can receive an input, but anexternal product cannot receive an input. Example technologies forselecting the inputs are described in the above-referenced andincorporated '768 patent.

FIG. 1 illustrates an example layout of a suitable network diagram 100that may express a process of one example implementation. Standardnetwork characteristics may be used in the layout to add meaning todisplayed data. As shown, for example, the network includes a centraltime-directed axis 102, and a plurality of network nodes 104 thatexpress respective products of the process. The nodes may be connectedby edges reflecting precedence relationships between the nodes, andcorrespondingly between the respective products of the process. Each ofthe network nodes may include an associated slack parameter that may beused to determine a distance of the network node from centraltime-directed axis. In this regard, nodes 106 having zero slack valuesmay be selected to lie on or near the axis, and other nodes 108 havinghigher slack values may be positioned about the axis. In one example,nodes 106 may be strictly-ordered nodes without flexibility in theirorder (linearly constrained), and the axis may be a linear,strictly-ordered axis. In this example, the other nodes 108 may beparallel nodes that have some flexibility in their order.

FIG. 2 illustrates a suitable network diagram 200 similar to diagram100, but that may express a more complex process. As shown, similar tothe diagram of FIG. 1, the network diagram of FIG. 2 a plurality ofnetwork nodes 202 that may be connected by edges reflecting precedencerelationships between the nodes, a portion of which are furtherhighlighted in inset 204. For more information regarding the layouts ofFIGS. 1 and 2, as well as other suitable layouts according to exampleimplementations, see U.S. Pat. No. 7,873,920, entitled: Methods andSystems for Displaying Network Information, issued Jan. 18, 2011, thecontent of which is hereby incorporated by reference in its entirety.For other example layouts of suitable network diagrams, see U.S. Pat.No. 8,581,904, entitled: Three-Dimensional Display of Specifications ina Scalable Feed-Forward Network, issued Nov. 12, 2013, the content ofwhich is hereby incorporated by reference in its entirety.

In a network diagram such as the network diagrams 100, 200 of FIGS. 1and 2, the nodes 104, 202 may represent the tasks to produce theinternal products. The edges connecting nodes, then, may represent theinternal products and reflect the precedence relationships betweentasks. For example, an internal product that is utilized or required forproduction of another internal product may be represented by an edgeconnecting nodes representing the tasks to produce the respectiveinternal product and other internal product. In this example, the taskto produce the internal product may be considered a predecessor, and thetask to produce the other internal product may be considered asuccessor. In this manner, the tasks (nodes) to produce internalproducts of the process expressed by the network may be properly orderedaccording to the internal products (edges) connecting them.

A process such as a hierarchical, well-ordered process according toexample implementations may be described by process-related informationand expressible as at least one network including a plurality of tasksto produce internal products corresponding to components of at least oneproduct produced thereby. As described above, at least some of the tasksmay have a precedence relationship whereby the internal product producedby one task is an input to another task. The process-related informationmay describe the internal products, external products, internalcomponents, component inputs and/or external components of the process.The process-related information may describe the tasks to produce theinternal product, and precedence relationships between tasks(predecessors, successors). In accordance with example implementations,the process-related information describes the plurality of tasks by aplurality of attributes (data fields).

As described below in Table 1, examples of suitable attributes includeas-late-as-possible (ALAP) position, as-soon-as-possible (ASAP)position, band number, input, location, name, organization, product,qualifier, uniform resource identifier (URI) and verb.

TABLE 1 Attribute Name Type Purpose ALAP Integer The band number thatcontains this position product when ordered as-late-as-possible. ASAPInteger The band number that contains this position product when orderedas-soon-as-possible. Band Integer A count of the sets of precedingparallel Number activities (e.g., band number one may include all taskswithout predecessors). Input URI List of inputs needed in order tocomplete the task. An input may also be a task. Location String Identifythe geographic or spatial location of a task or data item. Name StringProvide a name for a task or data item. Organization String Name oforganization performing the task. Product Object A product, task oractivity is a description of the act of producing a data deliverable.Qualifier String Modify the name of a task or data item. URI StringUniquely identify the task or data item. Verb String A word describingan action that is pre-pended to a data deliverable to describe the taskthat produces the data deliverable.

As explained above, a process may include emergent patterns of tasks andinputs, which may be referred to as “threaded lifecycle patterns” or“lifecycle threads” when considered in the lifecycle maturity phases ofproduct development. FIG. 3 illustrates a network diagram 300 of oneexample of a suitable lifecycle thread for the design and production ofa flight test, according to example implementations. Similar to before,the network includes a central time-directed axis 302, and a pluralityof network nodes 304 that express respective tasks to produce internalproducts of the process. The nodes are arranged along the time-directedaxis with tasks for the earliest or least mature products at the left,and tasks for the latest or most mature products at the right. In thecontext of a flight test process, for example, the node 306 thatexpresses one of the earliest tasks may be for release of a modeldefinition, detail design, and the node 308 for one of the latest if notthe terminal task may be update loadable software databases and models,product verification.

Also similar to before, the nodes 302 may be connected by edges 310reflecting precedence relationships between the nodes, andcorrespondingly between the respective tasks of the process. The edgesfor some precedence relationships are shown, while others are elided forclarity. In addition, the node 312 for a selected task may be identifiedin the network by a sphere 314 or other appropriate shape.

Example implementations of the present disclosure are directed toestablishment and replication of lifecycle threads, which may bedetected based on one or more attributes of the tasks. FIG. 4illustrates a lifecycle thread establishment and replication system 400according to example implementations. The lifecycle thread establishmentand replication system may be configured to perform a number ofdifferent functions or operations, either automatically, under directoperator control, or some combination of thereof. In this regard, thesystem may be configured to perform one or more of its functions oroperations automatically, that is, without being directly controlled byan operator. Additionally or alternatively, the system may be configuredto perform one or more of its functions or operations under directoperator control.

The lifecycle thread establishment and replication system 400 mayinclude one or more of each of any of a number of different subsystems(each an individual system) for performing one or more functions oroperations of the lifecycle thread establishment and replication system.As shown, for example, the system may include a source lifecycle threadsubsystem 402 and a target lifecycle thread subsystem 404 coupled to oneanother. Although being shown together as part of the system, it shouldbe understood that either of the subsystems may function or operate as aseparate system without regard to the other. And further, it should beunderstood that the system may include one or more additional oralternative subsystems than those shown in FIG. 4.

The source lifecycle thread subsystem 402 may be configured to define asource lifecycle thread from process-related information. As describedabove, this process-related information describes at least onehierarchical, well-ordered process expressible as at least one networkincluding a plurality of tasks to produce internal productscorresponding to components of at least one product produced thereby.And at least some of the plurality of tasks having a precedencerelationship whereby the internal product produced by one task is aninput to another task. The source lifecycle thread subsystem maytherefore establish a source lifecycle thread.

The target lifecycle thread subsystem 404 may be configured to defineand match a target lifecycle thread to the source lifecycle thread. Thetarget lifecycle thread may thereby replicate the source lifecyclethread to establish the target lifecycle thread. The source lifecyclethread and target lifecycle thread may each be expressible as asub-network of respectively source tasks and target tasks selected fromthe plurality of tasks. The target tasks have at least a thresholdsimilarity with the source tasks, and inputs to the target tasks have atleast a threshold similarity with inputs to the source tasks.

To establish either or both of the source lifecycle thread or targetlifecycle thread, either or both the source lifecycle thread subsystem402 or the target lifecycle thread subsystem 404 may include aclustering subsystem. FIG. 5 illustrates one example of a suitableclustering subsystem 500 according to example implementations of thepresent disclosure.

Similar to the lifecycle thread establishment and replication system400, the clustering subsystem 500 may be configured to perform a numberof different functions or operations, either automatically, under directoperator control, or some combination of thereof. Likewise, theclustering subsystem may include one or more of each of any of a numberof different subsystems (each an individual system). As shown, forexample, the clustering subsystem may include an input interface 502,cluster analysis engine 504 and task selector 506 coupled to oneanother, although any of the aforementioned may function or operate as aseparate system without regard to the other. And further, the clusteringsubsystem may include one or more additional or alternative subsystemsthan those shown in FIG. 5.

The input interface 502 may be configured to receive user selection ofan attribute of the plurality of attributes as user-selected criteria.From the above Table 1, examples of suitable attributes include name,qualifier, location, organization, ASAP position, ALAP position, verb,URI or the like. The cluster analysis engine 504 may be configured toperform a cluster analysis according to the user-selected criteria toproduce a plurality of clusters of candidate tasks from the plurality oftasks.

Cluster analysis or “clustering” is the task of grouping a set ofobjects in such a way that objects in the same group (called a cluster)are more similar (in some sense or another) to each other than to thosein other groups (clusters). Cluster analysis is not an automatic task,but an iterative process of knowledge discovery or interactivemulti-objective optimization that involves trial and failure. Tofacilitate this knowledge discovery, the user-selected criteria mayinclude one or more attributes such as those identified above. The taskselector may then be configured to select tasks from the plurality ofclusters of candidate tasks for the source tasks or target tasks.

FIG. 6 more particularly illustrates the cluster analysis engine 504according to some example implementations. Again, the cluster analysisengine may be configured to perform a number of different functions oroperations, either automatically, under direct operator control, or somecombination of thereof. Likewise, the cluster analysis engine mayinclude one or more of each of any of a number of different subsystems(each an individual system). As shown, for example, the cluster analysisengine may include a grouping engine 602, distance map generator 604 andtask organizer 606 coupled to one another, although any of theaforementioned may function or operate as a separate system withoutregard to the other. And further, the cluster analysis engine mayinclude one or more additional or alternative subsystems than thoseshown in FIG. 6.

Some example implementations of the present disclosure introduce theconcept of a grouping that maintains and contains unique value distancemaps for each attribute selected by the user as clustering criteria. Asdescribed above, each unique value distance map may be a mapping betweenthe unique values of an attribute and the distances between those values(as defined by each attribute's distance metric implementation). Inaccordance with these example implementations, then, the grouping engine602 may be configured to form a grouping of the plurality of tasksaccording to the attribute. The distance map generator 604 may beconfigured to generate a distance map for the grouping including onlyunique values of the attribute and distances between the unique values,and in some examples, these unique values may be normalized and perhapsalso squared to form normalized quadrance components.

Distance is a quantification of the magnitude of dissimilarity betweentwo products. The computation of distance may be established for eachattribute, and determined based on the data type of the attribute andthe objective of the cluster analysis. For example, the Levenshteindistance between two words is the minimum number of single-characteredits (insertions, deletions or substitutions) required to change oneword into the other. An example distance function used with phrases isbased on a count of the number of words in common. Specifically, thisdistance function is calculated by dividing twice the count of wordsdifferent between two product names by the sum of the unique word countin each product name. An example distance function used with verbs isbinary—either the verbs are the same (distance zero) or the verbs aredifferent (distance one).

Unique values are those values of distances for unique relationships. Anexample of normalized unique values for URI distance is shown below inTable 2.

TABLE 2 from URI 0.8.68.1: to 0.3.1.5 = 1 to 0.8.60.6 = 0.444 to0.8.68.2 = 0.111 to 0.8.60.5 = 0.444 to 0.3.1.2 = 1 to 0.8.60.4 = 0.444to 0.8.68.3 = 0.111 to 0.3.1.3 = 1 to 0.8.60.3 = 0.444 to 0.8.68.4 =0.111 to 0.3.1.1 = 1 to 0.8.60.2 = 0.444 to 0.8.68.5 = 0.111 to 0.8.68.6= 0.111 to 0.8.60.1 = 0.444 to 0.8.68.7 = 0.111

As each URI is unique, the distance between every URI is a unique value,and the value is stored in the second column, the values {0.111, 0.444,1}. Before normalization, the URI distance is a count of the differencesbetween values of the URI's at each level of the hierarchy. As only thelast digit differs, the distance between URI 0.8.68.1 and 0.8.68.5 is 1.Because two digits differ, the difference between URI 0.8.68.1 and0.8.60.6 is 2. As three digits differ, the distance between URI 0.8.68.1and 0.3.1.5 is 3. These distances are normalized and squared so they maybe stored as normalized quadrance components 1²/3², 2²/3² and 3²/3² forthe aforementioned normalized values of {0.111, 0.444, 1}.

In contrast to every URI as a unique value, there are a limited numberof verbs associated with products, and many products use the same verb.For verbs, the distance between different verbs may be one, while thedistance between the same verb may be zero. Memory and data storagerequirements are collapsed by storing only these unique distance values.And by storing these distance values as normalized quadrance components,example implementations reduce interactive processing to an ordinalcomparison, and thereby avoids the computational-intensive square root,and many multiplication and addition steps.

Regardless of the exact manner in which the distance map generator 604is configured to generate the distance map, the task organizer 606 maybe configured to organize the tasks into the plurality of clusters ofcandidate tasks according to distance using the distance map. The taskorganizer may organize the tasks in any of a number of differentsuitable manners, such as in accordance with any suitabledistance-clustering algorithm. One example of a suitabledistance-clustering algorithm is provided below.

In some examples, the input interface 502 may be configured to receiveuser selection of a plurality of attributes as the user-selectedcriteria. In these examples, the grouping engine 602 of the clusteranalysis engine 504 may be configured to form a respective plurality ofgroupings according to the plurality of attributes. The distance mapgenerator 604 may be configured to generate a distance map for eachgrouping of the respective plurality of groupings. The task organizer606 may be configured to organize the tasks includes being caused toorganize the tasks using the distance map for each grouping. And thetask selector 506 may be configured to select the tasks from a selectedcluster of the plurality of clusters.

In some examples, the input interface 502 may be configured to receiveuser selection of the plurality of attributes and a weight for aparticular attribute thereof as the user-selected criteria. In someexamples, the weight (or weights for multiple attributes) may beuser-selectable for a normalized value in the range of 0 to 1. In thismanner, a user who desires to evaluate clusters where the name providesa two times stronger association than the location, the weights of thename and location attributes may be set to respectively 1 and ½. Inthese examples, then, the cluster analysis engine 504 may furtherinclude a weight applicator 608 configured to apply the weight to thedistance map for the grouping according to the particular attribute. Thetask organizer 606, then, may be configured to organize the tasks usingthe distance map for each grouping including the distance map with theweight so applied.

In some further examples, the input interface 502 may be furtherconfigured to receive user selection of an adjusted weight for theparticular attribute. In these examples, the weight applicator 608 maybe further configured to apply the adjusted weight to the distance mapfor the grouping according to the particular attribute. And the taskorganizer 606 may be further configured to reorganize the tasks usingthe distance map for each grouping including the distance map with theadjusted weight so applied.

In some examples, each of the source lifecycle thread subsystem 402 andthe target lifecycle thread subsystem 404 may include a respectiveclustering subsystem 500. In these examples, in an instance in which theattribute received for both the source lifecycle thread and targetlifecycle thread is the same, the task organizer 606 of the clusteringsubsystem for the target lifecycle thread subsystem may organize thetasks using the distance map generated during the cluster analysis ofthe clustering subsystem for the source lifecycle thread without thedistance map generator 604 again generating the distance map during thecluster analysis for the target lifecycle thread.

FIG. 7 illustrates a target lifecycle thread subsystem 700 that in someexamples may correspond to the target lifecycle thread subsystem 404 ofFIG. 4. As with other subsystems described herein, the target lifecyclethread subsystem may be configured to perform a number of differentfunctions or operations, either automatically, under direct operatorcontrol, or some combination of thereof. Likewise, the target lifecyclethread subsystem may include one or more of each of any of a number ofdifferent subsystems (each an individual system). As shown, for example,the target lifecycle thread subsystem may include a candidate selector702, matching engine 704 and back-chaining engine 706 coupled to oneanother, although any of the aforementioned may function or operate as aseparate system without regard to the other. And further, the targetlifecycle thread subsystem may include one or more additional oralternative subsystems than those shown in FIG. 7.

The candidate selector 702 may be configured to select a plurality ofcandidate target tasks from the plurality of tasks. In some examples,the candidate selector may be implemented by the clustering subsystem500. In these examples, the plurality of candidate target tasks may beselected from the plurality of clusters of candidate tasks.

The matching engine 704 may be configured to match a candidate targettask to a particular source task of the source lifecycle thread. In someexamples, the matching engine may be configured to match the candidatetarget task to the particular source task using a distance map for anattribute of the plurality of attributes, with the distance mapincluding only unique values of the attribute and distances between theunique values. In some examples in which the candidate selector isimplemented by the clustering subsystem 500, the distance map may be thesame as that generated by the distance map generator 604 of the clusteranalysis engine 504.

The back-chaining engine 706 may be configured to back-chain through thesource lifecycle thread. For each source task of at least some of thesource tasks beginning with the particular source task, this may includethe back-chaining engine being configured to at least identify apreceding source task that inputs to the source task. The matchingengine 704 may then match another candidate target task to the precedingsource task, such as with use of the distance map. In some examples inwhich the target tasks have a well order, the matching engine may beconfigured to match the other candidate target task that maintains thewell order. Through this process, the target lifecycle thread may becomposed of the candidate target task and other candidate target taskmatched to the preceding source task for each source task of the atleast some of the source tasks.

In some examples, the matching engine 704 may be configured to removethe candidate target task from the plurality of candidate target tasksafter the candidate target task is matched to the particular sourcetask. Likewise, in these examples, the matching engine may be configuredto remove the other candidate target task from the plurality ofcandidate target tasks after the other candidate target task is matchedto the preceding source task. Moreover, in at least one instance inwhich the candidate target task and other candidate target task areprecedence unrelated, the matching engine may be further configured todefine an input to the candidate target task from the other candidatetarget task to thereby establish a precedence relationship.

FIGS. 8A and 8B are flowcharts illustrating various steps in a method800 according to example implementations of the present disclosure, andwhich are described in the context of the apparatus 1800 shown anddescribed below with reference to FIG. 18. As shown at block 802, themethod includes accessing, by processing circuitry 1802, a memory 1804storing process-related information describing at least onehierarchical, well-ordered process. The process is expressible as atleast one network including a plurality of tasks to produce internalproducts corresponding to components of at least one product producedthereby. In the network, at least some of the plurality of tasks havinga precedence relationship whereby the internal product produced by onetask is an input to another task.

As shown at block 804, the method includes defining, by the processingcircuitry 1802, a source lifecycle thread from the process-relatedinformation. The method also includes defining and matching, by theprocessing circuitry, a target lifecycle thread to the source lifecyclethread, as shown at block 806. The source lifecycle thread and targetlifecycle thread are each expressible as a sub-network of respectivelysource tasks and target tasks selected from the plurality of tasks. Thetarget tasks have at least a threshold similarity with the source tasks,and inputs to the target tasks have at least a threshold similarity withinputs to the source tasks.

The process-related information describes the plurality of tasks by aplurality of attributes. Either or each of defining the source lifecyclethread (block 804), or defining and matching the target lifecycle thread(block 806), includes at least receiving user selection of an attributeof the plurality of attributes as user-selected criteria, as shown atblock 808. Here, the method also includes performing a cluster analysisaccording to the user-selected criteria to produce a plurality ofclusters of candidate tasks from the plurality of tasks, and selectingtasks from the plurality of clusters of candidate tasks for the sourcetasks or target tasks, as shown at blocks 810 and 812. And according tothese example implementations, performing the cluster analysis includingforming a grouping of the plurality of tasks according to the attribute,generating a distance map for the grouping including only unique valuesof the attribute and distances between the unique values, and organizingthe tasks into the plurality of clusters of candidate tasks according todistance using the distance map.

FIGS. 9A and 9B are flowcharts illustrating various steps in a method900 according to example implementations of the present disclosure. Asshown at block 902, the method includes accessing, by processingcircuitry 1802, a memory 1804 storing process-related informationdescribing at least one hierarchical, well-ordered process. The processis expressible as at least one network including a plurality of tasks toproduce internal products corresponding to components of at least oneproduct produced thereby. In the network, at least some of the pluralityof tasks having a precedence relationship whereby the internal productproduced by one task is an input to another task.

As shown at block 904, the method includes defining, by the processingcircuitry 1802, a source lifecycle thread from the process-relatedinformation. The method also includes defining and matching, by theprocessing circuitry, a target lifecycle thread to the source lifecyclethread, as shown at block 906. The source lifecycle thread and targetlifecycle thread are each expressible as a sub-network of respectivelysource tasks and target tasks selected from the plurality of tasks. Thetarget tasks have at least a threshold similarity with the source tasks,and inputs to the target tasks have at least a threshold similarity withinputs to the source tasks.

The process-related information describes the plurality of tasks by aplurality of attributes. Defining and matching the target lifecyclethread (block 906) includes at least selecting a plurality of candidatetarget tasks from the plurality of tasks, as shown at block 908. Here,the method also includes matching a candidate target task of theplurality of candidate target tasks to a particular source task of thesource lifecycle thread using a distance map for an attribute of theplurality of attributes, with the distance map including only uniquevalues of the attribute and distances between the unique values, asshown at block 910.

The method here also includes back-chaining through the source lifecyclethread, as shown at block 912. For each source task of at least some ofthe source tasks beginning with the particular source task, thisback-chaining may include identifying a preceding source task thatinputs to the source task, and matching another candidate target task ofthe plurality of candidate target tasks to the preceding source taskusing the distance map. The target lifecycle thread, then, may becomposed of the candidate target task and other candidate target taskmatched to the preceding source task for each source task of the atleast some of the source tasks.

To further illustrate operations in methods that may be performed by thesource lifecycle thread subsystem 402, target lifecycle thread subsystem404 and clustering subsystem 500, reference is now made to respectivelyFIGS. 10, 11 and 12.

FIG. 10 is a flowchart illustrating various operations in a routine 1000that may be performed by or with the source lifecycle thread subsystem402, according to example implementations of the present disclosure. Asshown at block 1002, the routine may include selection of a sourcenetwork, process and hierarchical level, such as from a model of ahierarchical, well-ordered process. This source network may contain aplurality of processes in a plurality of hierarchical levels, with aprocess at a particular level being containing a lifecycle thread to beidentified as the source lifecycle thread.

As shown at block 1004, user-selected criteria including one or moreattributes and perhaps weights may be received for performance of acluster analysis to cluster tasks based thereon. A user may select oneor more clusters as candidates for the source lifecycle thread, as shownat block 1006. Here, the selected cluster(s) may contain tasks (sourcetasks) intended for the source lifecycle thread. The user may then havethe option to review and refine the source lifecycle thread, such as byindividual selection of tasks from a cluster to include or exclude fromthe source lifecycle thread, as shown at block 1008. As each task(source task) is selected for the source lifecycle thread, inputs tothat task may be listed, enabling the user to include or exclude theinputs from the source lifecycle thread.

As shown at block 1010, a decision may be made as to whether all sourcetasks are included in the source lifecycle thread. In some examples,this decision is made based on the user's knowledge of the processintended to be captured by the source lifecycle thread. The user mayverify that the earliest source task in the source lifecycle threadcorresponds to an entry point in the process, that the latest orterminal source task in the source lifecycle thread corresponds to anexit point in the process, and that all necessary intermediate tasks areincluded. If all source tasks are included, the source lifecycle threadmay be named and stored, as shown at block 1012. Otherwise, in the eventthat more source tasks are needed, the routine may return to clustertasks on user-selected criteria (at block 1004), at which point the usermay adjust the user-selected criteria or select different clusters forthe source lifecycle thread.

FIG. 11 is a flowchart illustrating various operations in a routine 1100that may be performed by or with the target lifecycle thread subsystem404, according to example implementations of the present disclosure. Asshown at block 1102, the routine may include selection of a targetnetwork, process and hierarchical level from a model of a hierarchical,well-ordered process, which may be the same or a different model fromwhich the source network was selected. This source network may contain aplurality of processes in a plurality of hierarchical levels, with aprocess at a particular level containing a lifecycle thread to beidentified as the source lifecycle thread. Then as shown at block 1104,the routine may include receipt of user-selected criteria, includingattribute(s) and/or weight(s), and performance of a cluster analysisbased thereon. In some examples, the operations at 1102 and 1104 may beperformed in a manner similar to those for the source lifecycle threadat 1002 and 1004, including identification of a process at a particularlevel of a target network containing a plurality of processes at aplurality of hierarchical levels.

As shown at block 1106, the cluster analysis for the target lifecyclethread may produce a list of candidate target tasks. In some examples,the list of candidate target tasks is produced through one or moredistance maps, which enables the user to change candidates based oninteractive knowledge discovery, as described above. In other examples,the list of candidate target tasks is produced through string similarityof the task names. In these other examples, string similarity may bedetermined by dividing twice the count of words different between twotask names by the sum of the unique word count in each name.

After production of the candidate target tasks, a determination may thenbe made if the candidate target tasks include a candidate target taskmatching a source task from the source lifecycle thread, as shown atblock 1108. A match may be determined based on a measure of similaritybetween the candidate target task and source task, such as through thedistance map(s) or string similarity. In some examples, the match may bedetermined by the user based on the user's knowledge of the overallmodel and naming conventions to associate task names with the targettasks.

In instances in which there is no match, the routine 1100 may return toclustering tasks on user-selected criteria (block 1104), at which pointthe user may adjust the user-selected criteria or select differentclusters for the target lifecycle thread. On the other hand, ininstances in which there is a match, the match may be confirmed, and theuser may be prompted to name the target lifecycle thread for storage, asshown at block 1110. The target lifecycle thread subsystem 404 may thenproduce a list of candidate target inputs, as shown at block 1112.Similar to before, this list may be produced through distance map(s) orstring similarity of task names. In some examples, this list may also beconstrained to maintain well-ordering of the target tasks in the targetlifecycle thread.

As shown at block 1114, the routine 1100 may then include adetermination of a match of the candidate target inputs and an input tothe matched source task (block 1108). Similar to before, this match maybe determined based on a measure of similarity between the candidatetarget task and source task, such as through the distance map(s) orstring similarity. In some examples, the match may be determined by theuser based on the user's knowledge of the overall model and namingconventions to associate task names with the target tasks. In instancesin which a match is found, the match may be confirmed and any missinginput between the matched candidate target task and the matchingcandidate target input may be defined, as shown at block 1116. Theroutine may then continue for each unmatched source input in the sourcelifecycle thread, as shown at block 1118, and then back up to block1112.

FIG. 12 is a flowchart illustrating various operations in a subroutine1200 that may be performed by or with the clustering subsystem 500,according to example implementations of the present disclosure. Asshown, for example, the subroutine may include a determination ofwhether to change the user-selected criteria such as one or moreattributes on which the tasks are clustered, as shown at block 1202.This may be the case when previous clusters do not collect together adesired set of tasks, and in which distance maps may have been generatedfor groupings of one or more attributes selected as user-selectedcriteria.

In instances in which the attributes on which the tasks are clusteredchanges, groupings may be created based on the attributes in theuser-selected criteria, as shown at block 1204. The subroutine 1200 maythen calculate and map distances between unique values within thegrouping for each attribute of the user-selected criteria, as shown atblock 1206. As discussed above, this map stores the unique values ofdistance for each selected attribute.

The subroutine 1200 may normalize the distances within each grouping,such as to adjust the values to range between 0.0 and 1.0, as shown atblock 1208. In some examples, the normalized values may also be squaredto thereby produce quadrance components. Any weights for the attributesof the user-selected criteria may be applied, as shown at block 1210. Asdiscussed above, the attributes and/or weights may be adjusted in aprocess of trial and failure until clusters of lifecycle thread targetsare found.

The tasks may then be organized into clusters using the distance mapswith any weights applied. More particularly, as shown at block 1212, thesubroutine 1200 may find the nearest neighbor of each product to linkall products into neighborhoods. In some examples, the nearest neighboris found by iterating through the tasks and finding a task with aminimum distance according to the user-selected clustering criteria(attributes and any weights). Several tasks may have the same minimumdistance; and in that case, any task with a minimum distance may beselected.

The subroutine 1200 may create a cluster for each neighborhood, whichmay collect together tasks into clusters of nearest neighbors accordingto the user-selected criteria, as shown at block 1214. A manifest maycontain and maintain groupings and clusters in their current state, andthis manifest may be updated after the cluster is created for eachneighborhood, as shown at block 1216. A depiction of each cluster may becreated, with the depiction being a graphical container collecting thetasks in a cluster, as shown at block 1218. And then the subroutine maylayout and display the clusters, such as using a force-directed layout,as shown at block 1220. A force-directed layout simulates repulsiveforces between tasks and simulates springs or attractive forces betweenneighbors, which arranges the tasks in three-dimensional space. Thisresults in a display of the clusters.

Further to the above, the manifest created and updated throughout thesubroutine 1200 contains and maintains grouping information and thecurrent clustering state. The grouping construct may be instantiated foreach user-selected attribute, and each may include a mapping between theunique values of a respective attribute and the distances between thosevalues (as defined by each attribute's distance metric implementation).The clustering state, then, may be the association of tasks withclusters. Table 2 above provides an example of the mapping between URI0.8.68.1 and all other unique URI's. A grouping is responsible for asimilar map for every other URI within Table 2.

The manifest and its grouping and distance map structure may requirecomputationally-demanding distance calculations, but may also be createdonly once per user session (e.g., during its initialization). The uniquevalue distance mapping of the grouping may maintains distances asquadrance components for each attribute, eliminating the need forrepeated squaring and performance of square-root-computation. Uniquevalue mapping (storing only unique values rather than attributedistances for every task) also reduces memory requirements needed forprocessing. This allows a user to quickly and interactively adjustrelative attribute weights, achieving desired clustering results.

To even further illustrate example implementations, reference is nowmade to FIGS. 13-17, which illustrate displays of a graphical userinterface (GUI) that may be provided by the lifecycle threadestablishment and replication system 400 during an interactive usersession in which the above routines 1000, 1100 and subroutine 1200 maybe performed. In these figures, the operations are performed in thecontext of processes for design and construction of a house, includingprocesses for design and installation of carpet and shoe base mouldingin a room of a house, which are further examples of suitable processesfor production of a product (a house).

FIG. 13 illustrates a display 1300 of the GUI including the results ofclustering tasks for the design and installation of carpet using the URIattribute and a weight of 1.0 as the user-selected criteria, which areshown in a panel 1302 of the GUI. But as the tasks in this example areclustered on only a single attribute, the weight has no practical effecton the resulting clusters. The GUI also illustrates various clusters1304 that have been produced. A selected cluster of interest forestablishing a source lifecycle thread is shown as 1306. Hover text 1308enables the user to inspect the tasks within a cluster to find thecluster of interest, and a search box 1310 allows the user to search fora URI of interest within the clusters. When a cluster is selected, theuser may add tasks into the source lifecycle thread using the checkboxesin panel 1312.

Table 3 below shows the selected cluster from FIG. 13 in tabular format.Each URI is unique and has a distance of 0.111111 from its nearestneighbor.

TABLE 3 URI Neighbor Distance Name Organization Verb 0.8.68.1 0.8.68.20.111111 Objectives General Establish Contractor Admin X 0.8.68.20.8.68.6 0.111111 Designs & Specs. Designer X Develop 0.8.68.3 0.8.68.20.111111 Contract General Conduct Agreement Contractor Admin X 0.8.68.40.8.68.2 0.111111 Materials & Carpet Sub X Procure and Supplies Deliver0.8.68.5 0.8.68.2 0.111111 Installation Carpet Sub X Perform 0.8.68.60.8.68.3 0.111111 Notification - Carpet Sub X Communicate Completion0.8.68.7 0.8.68.2 0.111111 Inspection - General Contract ContractorCompletion Inspector XEach URI distance is likewise unique and stored in a distance map, apart of which is excerpted as Table 2 (above). This shows that thedistance from URI 0.8.68.1 to other URI's are as much as 1.0, and assmall as 0.111, making any URI with a value of 0.111 a nearest neighbor.

FIG. 14 illustrates a display 1400 of the GUI including the tasks andinputs for an example source lifecycle thread for the design andinstallation of carpet. A hierarchy of data deliverables is shown in atree 1402 that includes the tasks 1404 for the carpet lifecycle. Thebreakdown of the tasks and their relationships are illustrated in thebreakdown layout 1406, which is one example of a layout as described inthe above-referenced '904 patent. In this layout, for example, task 1408requires inputs from tasks 1410 and 1412.

FIG. 15 illustrates a display 1500 of the GUI including the results ofclustering tasks for the design and installation of shoe base mouldingusing the verb attribute and a weight of 1.0 as the user-selectedcriteria, which are shown in a panel 1502 of the GUI. Again, as thetasks in this example are clustered on only a single attribute, theweight has no practical effect on the resulting clusters. The GUI alsoillustrates various clusters 1504 that have been produced. A selectedduster of interest for establishing a target lifecycle thread is shownas 1506. Hover text 1508 enables the user to inspect the tasks within acluster to find the cluster of interest, and the search box 1510 allowsthe user to search for a URI of interest within the clusters. When acluster is selected, the user may add tasks into the candidate targettasks using the checkboxes in panel 1512.

Table 4 below shows the selected cluster from FIG. 15 in tabular format.In this cluster, the tasks have no verb assigned, and have a distance of0.0 from each nearest neighbor.

TABLE 3 URI Neighbor Distance Name Organization 0.1.1.1 0.3.1.4 0.0Marketing & General Contractor Sales Admin X 0.1.1.2 0.3.1.4 0.0Contract General Contractor Agreement Admin X 0.2.1.1 0.3.1.4 0.0 Meetwith General Contractor Owner and DRB Establish Project Vision/Goals0.2.1.2 0.3.1.4 0.0 Prepare Design Subdivision X Guidelines for ProjectX 0.8.60.2 0.3.1.4 0.0 Designs & Designer X Specs. 0.8.60.3 0.3.1.4 0.0Contract General Contractor Agreement Admin X 0.8.60.4 0.3.1.4 0.0Materials & General Contractor Supplies Frame Crew A 0.8.60.5 0.3.1.40.0 Installation General Contractor Frame Crew A 0.8.60.6 0.3.1.4 0.0Notification - General Contractor Completion Frame Crew A 0.8.60.70.3.1.4 0.0 Inspection - General Contractor Contract Inspector XCompletion 0.8.68.7 0.3.1.4 0.0 Inspection - General Contractor ContractInspector X Completion 0.8.69.5 0.3.1.4 0.0 Installation GeneralContractor Finish Crew A 0.8.69.6 0.3.1.4 0.0 Notification - GeneralContractor Completion Finish Crew A 0.8.69.7 0.3.1.4 0.0 Inspection -General Contractor Contract Inspector X Completion 0.9.1.1 0.3.1.4 0.0Objectives General Contractor Admin X 0.9.1.2 0.3.1.4 0.0 Designs &Specs. Designer X 0.9.1.3 0.3.1.4 0.0 Contract General ContractorAgreement Admin X 0.9.1.4 0.3.1.4 0.0 Materials & Construction SuppliesCleaning Sub X 0.9.1.5 0.3.1.4 0.0 Installation Construction CleaningSub X 0.9.1.6 0.3.1.4 0.0 Notification - Construction CompletionCleaning Sub X

All non-zero distances are stored in a distance map in which thedistance between different verbs may be 1.0, and by omission thedistance between the same verb, or in this case the distance betweentasks with no assigned verb, may be 0.0. This is shown in Table 4 below.

TABLE 4 From “Establish”: to “Develop” = 1 From “Develop”: From“Perform”: to “Establish” = 1 to “Communicate” = 1 to “Procure andDeliver” = 1 to “Conduct” = 1 From “Communicate” to “Establish” = 1 to“Develop” = 1 to “Procure and Deliver” = 1 From “Procure and Deliver” to“Establish” = 1 to “Develop” = 1 From “Conduct” to “Establish” = 1 to“Develop” = 1 to “Communicate” = 1 to “Procure and Deliver” = 1

Table 5 below shows two clusters resulting from setting the weight forverb to 0.5, and URI to 1.0. In the table, a distance of 0.6111 can beseen between neighbors 0.8.60.1 and 0.8.60.2. This distance may becalculated by taking half of the verb distance of 1.0 (Table 4), plusthe distance of 0.111 from a distance map for URI. As the distancetables are unchanged, this demonstrates how the weights may be adjustedwithout again generating the distance maps.

TABLE 5 URI Neighbor Distance Name Organization Verb 0.8.60.1 0.8.60.20.611111 Objectives General Contractor Establish Admin X 0.8.60.20.8.68.2 0.444444 Designs & Designer X Develop Specs. 0.8.60.3 0.8.60.20.611111 Contract General Contractor Conduct Agreement Admin X 0.8.60.40.8.68.4 0.444444 Materials & General Contractor Procure and SuppliesFrame Crew A Deliver 0.8.60.6 0.8.60.2 0.611111 Notification - GeneralContractor Communicate Completion Frame Crew A 0.8.68.1 0.8.60.10.444444 Objectives General Contractor Establish Admin X 0.8.68.20.8.68.6 0.611111 Designs & Designer X Develop Specs. 0.8.68.3 0.8.60.30.444444 Contract General Contractor Conduct Agreement Admin X 0.8.68.40.8.68.6 0.611111 Materials & Carpet Sub X Procure and Supplies Deliver0.8.68.5 0.8.68.6 0.611111 Installation Carpet Sub X Perform 0.8.68.60.8.60.6 0.444444 Notification - Carpet Sub X Communicate Completion URINeighbor Distance Name Organization 0.1.1.1 0.7.4.2 1.0 Marketing &General Contractor Admin X Sales 0.1.1.2 0.1.1.1 0.111111 ContractGeneral Contractor Admin X Agreement 0.4.1.1 0.4.1.2 0.111111 CompleteDesigner X Prelim. Design & Spec. for X 0.4.1.2 0.7.4.2 1.0 ApprovePrelim. Owner X Design & Spec for X and Notify Designer X 0.7.4.10.7.4.2 0.111111 Objectives General Contractor Admin X 0.7.4.2 0.7.4.70.111111 Designs & Designer X Specs. 0.7.4.3 0.7.4.2 0.111111 ContractGeneral Contractor Admin X Agreement 0.7.4.4 0.7.4.2 0.111111 Materials& Landscape Sub X Supplies 0.7.4.5 0.7.4.2 0.111111 Layout GeneralContractor Layout Crew X 0.7.4.6 0.7.4.2 0.111111 Installation LandscapeSub X 0.7.4.7 0.7.4.4 0.111111 Notification - Landscape Sub X Completion0.7.4.8 0.7.4.2 0.111111 Inspection - General Contractor Inspector XContract Completion

FIG. 16 illustrates a display 1600 of the GUI including the tasks andinputs for an example target lifecycle thread for the design andinstallation of shoe base moulding. A hierarchy of data deliverables isshown in a tree 1602 that includes the tasks 1604 for the carpetlifecycle. The breakdown of the tasks and their relationships areillustrated in the breakdown layout 1606, which, again, is one exampleof a layout as described in the above-referenced '904 patent. In thislayout, for example, relationships between the tasks have not yet beendefined, but will be defined as the target lifecycle thread is matchedto the source lifecycle thread.

FIG. 17 illustrates a display 1700 of the GUI illustrating candidatetarget tasks. The display includes a source lifecycle thread 1702(without precedences being shown). A particular source task 1704 underconsideration is indicated by a sphere 1706. In this example, theparticular source task is identified as “Perform Inspection for ContractCompletion CARPET, Products” at 1708. Two candidate target tasks areillustrated at 1710 and 1712; and identified as “Perform Inspection forContract Completion LOCKSETS AND DOORSTOPS, Products” and “PerformInspection for Contract Completion SHOE BASE MOULDING, Products” atrespectively 1714 and 1716. Similarly, the target lifecycle threadsubsystem 404 may indicate a 75% similarity at 1718, and may also do sographically by an amount of fill 1720 of the tetrahedron shape for thecandidate target tasks. The user may now determine whether to match thesource task to either of the candidate target tasks; and in thisexample, the candidate target task related to shoe base moulding may beselected as it relates to the subject matter of the target lifecyclethread.

According to example implementations of the present disclosure, thelifecycle thread establishment and replication system 400 and itssubsystems including the source lifecycle thread subsystem 402 andtarget lifecycle thread subsystem 404 may be implemented by variousmeans. Similarly, the clustering subsystem 500 and its subsystemsincluding input interface 502, duster analysis engine 504 and taskselector 506 may be implemented by various means. Means for implementingthe system and its subsystems may include hardware, alone or underdirection of one or more computer programs from a computer-readablestorage medium. In some examples, one or more apparatuses may beconfigured to function as or otherwise implement the system and itssubsystems shown and described herein. In examples involving more thanone apparatus, the respective apparatuses may be connected to orotherwise in communication with one another in a number of differentmanners, such as directly or indirectly via a wired or wireless networkor the like.

FIG. 18 illustrates an apparatus 1800 according to some exampleimplementations of the present disclosure. Generally, an apparatus ofexemplary implementations of the present disclosure may comprise,include or be embodied in one or more fixed or portable electronicdevices. Examples of suitable electronic devices include a smartphone,tablet computer, laptop computer, desktop computer, workstationcomputer, server computer or the like. The apparatus may include one ormore of each of a number of components such as, for example, processingcircuitry 1802 (e.g., processor unit) connected to a memory 1804 (e.g.,storage device).

The processing circuitry 1802 may be composed of one or more processorsalone or in combination with one or more memories. The processingcircuitry is generally any piece of computer hardware that is capable ofprocessing information such as, for example, data, computer programsand/or other suitable electronic information. The processing circuitryis composed of a collection of electronic circuits some of which may bepackaged as an integrated circuit or multiple interconnected integratedcircuits (an integrated circuit at times more commonly referred to as a“chip”). The processing circuitry may be configured to execute computerprograms, which may be stored onboard the processing circuitry orotherwise stored in the memory 1804 (of the same or another apparatus).

The processing circuitry 1802 may be a number of processors, amulti-core processor or some other type of processor, depending on theparticular implementation. Further, the processing circuitry may beimplemented using a number of heterogeneous processor systems in which amain processor is present with one or more secondary processors on asingle chip. As another illustrative example, the processing circuitrymay be a symmetric multi-processor system containing multiple processorsof the same type. In yet another example, the processing circuitry maybe embodied as or otherwise include one or more ASICs, FPGAs or thelike. Thus, although the processing circuitry may be capable ofexecuting a computer program to perform one or more functions, theprocessing circuitry of various examples may be capable of performingone or more functions without the aid of a computer program. In eitherinstance, the processing circuitry may be appropriately programmed toperform functions or operations according to example implementations ofthe present disclosure.

The memory 1804 is generally any piece of computer hardware that iscapable of storing information such as, for example, data, computerprograms (e.g., computer-readable program code 1806) and/or othersuitable information either on a temporary basis and/or a permanentbasis. The memory may include volatile and/or non-volatile memory, andmay be fixed or removable. Examples of suitable memory include randomaccess memory (RAM), read-only memory (ROM), a hard drive, a flashmemory, a thumb drive, a removable computer diskette, an optical disk, amagnetic tape or some combination of the above. Optical disks mayinclude compact disk-read only memory (CD-ROM), compact disk read/write(CD-R/W), DVD or the like. In various instances, the memory may bereferred to as a computer-readable storage medium. The computer-readablestorage medium is a non-transitory device capable of storinginformation, and is distinguishable from computer-readable transmissionmedia such as electronic transitory signals capable of carryinginformation from one location to another. Computer-readable medium asdescribed herein may generally refer to a computer-readable storagemedium or computer-readable transmission medium.

In addition to the memory 1804, the processing circuitry 1802 may alsobe connected to one or more interfaces for displaying, transmittingand/or receiving information. The interfaces may include acommunications interface 1808 (e.g., communications unit) and/or one ormore user interfaces. The communications interface may be configured totransmit and/or receive information, such as to and/or from otherapparatus(es), network(s) or the like. The communications interface maybe configured to transmit and/or receive information by physical (wired)and/or wireless communications links. Examples of suitable communicationinterfaces include a network interface controller (NIC), wireless MC(WNIC) or the like.

The user interfaces may include a display 1810 and/or one or more userinput interfaces 1812 (e.g., input/output unit). The display may beconfigured to present or otherwise display information to a user,suitable examples of which include a liquid crystal display (LCD),light-emitting diode display (LED), plasma display panel (PDP) or thelike. The user input interfaces may be wired or wireless, and may beconfigured to receive information from a user into the apparatus, suchas for processing, storage and/or display. Suitable examples of userinput interfaces include a microphone, image or video capture device,keyboard or keypad, joystick, touch-sensitive surface (separate from orintegrated into a touchscreen), biometric sensor or the like. The userinterfaces may further include one or more interfaces for communicatingwith peripherals such as printers, scanners or the like.

As indicated above, program code instructions may be stored in memory,and executed by processing circuitry that is thereby programmed, toimplement functions of the systems, subsystems, tools and theirrespective elements described herein. As will be appreciated, anysuitable program code instructions may be loaded onto a computer orother programmable apparatus from a computer-readable storage medium toproduce a particular machine, such that the particular machine becomes ameans for implementing the functions specified herein. These programcode instructions may also be stored in a computer-readable storagemedium that can direct a computer, processing circuitry or otherprogrammable apparatus to function in a particular manner to therebygenerate a particular machine or particular article of manufacture. Theinstructions stored in the computer-readable storage medium may producean article of manufacture, where the article of manufacture becomes ameans for implementing functions described herein. The program codeinstructions may be retrieved from a computer-readable storage mediumand loaded into a computer, processing circuitry or other programmableapparatus to configure the computer, processing circuitry or otherprogrammable apparatus to execute operations to be performed on or bythe computer, processing circuitry or other programmable apparatus.

Retrieval, loading and execution of the program code instructions may beperformed sequentially such that one instruction is retrieved, loadedand executed at a time. In some example implementations, retrieval,loading and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Executionof the program code instructions may produce a computer-implementedprocess such that the instructions executed by the computer, processingcircuitry or other programmable apparatus provide operations forimplementing functions described herein.

Execution of instructions by processing circuitry, or storage ofinstructions in a computer-readable storage medium, supportscombinations of operations for performing the specified functions. Inthis manner, an apparatus 1800 may include processing circuitry 1802 anda computer-readable storage medium or memory 1804 coupled to theprocessing circuitry, where the processing circuitry is configured toexecute computer-readable program code 1806 stored in the memory. Itwill also be understood that one or more functions, and combinations offunctions, may be implemented by special purpose hardware-based computersystems and/or processing circuitry which perform the specifiedfunctions, or combinations of special purpose hardware and program codeinstructions.

Many modifications and other implementations of the disclosure set forthherein will come to mind to one skilled in the art to which thedisclosure pertains having the benefit of the teachings presented in theforegoing description and the associated drawings. Therefore, it is tobe understood that the disclosure is not to be limited to the specificimplementations disclosed and that modifications and otherimplementations are intended to be included within the scope of theappended claims. Moreover, although the foregoing description and theassociated drawings describe example implementations in the context ofcertain example combinations of elements and/or functions, it should beappreciated that different combinations of elements and/or functions maybe provided by alternative implementations without departing from thescope of the appended claims. In this regard, for example, differentcombinations of elements and/or functions than those explicitlydescribed above are also contemplated as may be set forth in some of theappended claims. Although specific terms are employed herein, they areused in a generic and descriptive sense only and not for purposes oflimitation.

What is claimed is:
 1. An apparatus for processing data to establishlifecycle threads in the development of a structural product, theapparatus comprising: a memory storing process-related informationdescribing at least one hierarchical, well-ordered process expressibleas at least one network including a plurality of tasks to produceinternal products corresponding to components of at least one structuralproduct produced thereby, at least some of the plurality of tasks havinga precedence relationship whereby the internal product produced by onetask is an input to another task; and processing circuitry configured toaccess the memory, and programmed to at least: define a source lifecyclethread from the process-related information; and define and match atarget lifecycle thread to the source lifecycle thread, the sourcelifecycle thread and target lifecycle thread each being expressible as asub-network of respectively source tasks and target tasks selected fromthe plurality of tasks, the target tasks having at least a thresholdsimilarity with the source tasks, and inputs to the target tasks havingat least a threshold similarity with inputs to the source tasks, whereinthe process-related information describes the plurality of tasks by aplurality of attributes, and the processing circuitry being programmedto either or each of define the source lifecycle thread, or define andmatch the target lifecycle thread, includes being programmed to atleast: receive user selection of an attribute of the plurality ofattributes as user-selected criteria; perform a cluster analysisaccording to the user-selected criteria to produce a plurality ofclusters of candidate tasks from the plurality of tasks, the processingcircuitry being programmed to perform the cluster analysis includingbeing programmed to form a grouping of the plurality of tasks accordingto the attribute, generate a distance map for the grouping includingonly unique values of the attribute and distances between the uniquevalues, and organize the tasks into the plurality of clusters ofcandidate tasks according to distance using the distance map; and selecttasks from the plurality of clusters of candidate tasks for the sourcetasks or target tasks.
 2. The apparatus of claim 1, wherein theprocessing circuitry being programmed to receive user selection of theattribute includes being programmed to receive user selection of aplurality of attributes as the user-selected criteria, wherein theprocessing circuitry being programmed to form the grouping includesbeing programmed to form a respective plurality of groupings accordingto the plurality of attributes, being programmed to generate thedistance map includes being programmed to generate a distance map foreach grouping of the respective plurality of groupings, and beingprogrammed to organize the tasks includes being programmed to organizethe tasks using the distance map for each grouping, and wherein theprocessing circuitry being programmed to select the tasks includes beingprogrammed to select the tasks from a selected cluster of the pluralityof clusters.
 3. The apparatus of claim 2, wherein the processingcircuitry being programmed to receive user selection of the plurality ofattributes includes being programmed to receive user selection of theplurality of attributes and a weight for a particular attribute thereofas the user-selected criteria, and wherein the processing circuitrybeing programmed to perform the cluster analysis further includes beingprogrammed to apply the weight to the distance map for the groupingaccording to the particular attribute, and being programmed to organizethe tasks includes being programmed to organize the tasks using thedistance map for each grouping including the distance map with theweight so applied.
 4. The apparatus of claim 3, wherein the processingcircuitry being programmed to define the source lifecycle thread ordefine the target lifecycle thread further includes being programmed toreceive user selection of an adjusted weight for the particularattribute, and wherein the processing circuitry being programmed toperform the cluster analysis further includes being programmed to applythe adjusted weight to the distance map for the grouping according tothe particular attribute, and reorganize the tasks using the distancemap for each grouping including the distance map with the adjustedweight so applied.
 5. The apparatus of claim 1, wherein the processingcircuitry being programmed to each of define the source lifecyclethread, and define and match the target lifecycle thread, includes beingprogrammed to receive user selection of the attribute, perform thecluster analysis and select the tasks, and wherein in an instance inwhich the attribute received for both the source lifecycle thread andtarget lifecycle thread is the same, the processing circuitry beingprogrammed to define and match the target lifecycle thread includesbeing programmed to organize the tasks using the distance map generatedduring the cluster analysis for the source lifecycle thread withoutagain generating the distance map during the cluster analysis for thetarget lifecycle thread.
 6. The apparatus of claim 1, wherein theprocessing circuitry being programmed to define and match the targetlifecycle thread includes being programmed to at least: select aplurality of candidate target tasks from the plurality of tasks, theplurality of candidate target tasks being selected from the plurality ofclusters of candidate tasks in an instance in which the processingcircuitry being programmed to define and match the target lifecyclethread includes being programmed to perform the cluster analysis; matcha candidate target task to a particular source task of the sourcelifecycle thread; and back-chain through the source lifecycle threadincluding for each source task of at least some of the source tasksbeginning with the particular source task, the processing circuitrybeing programmed to at least: identify a preceding source task thatinputs to the source task; and match another candidate target task tothe preceding source task, the target lifecycle thread being composed ofthe candidate target task and other candidate target task matched to thepreceding source task for each source task of the at least some of thesource tasks.
 7. The apparatus of claim 6, wherein in at least oneinstance in which the candidate target task and other candidate targettask are precedence unrelated, the processing circuitry being programmedto back-chain through the source lifecycle thread further includes beingprogrammed to define an input to the candidate target task from theother candidate target task to thereby establish a precedencerelationship.
 8. A method of processing data to establish and replicatelifecycle threads in the development of a structural product, the methodcomprising: accessing, by processing circuitry, a memory storingprocess-related information describing at least one hierarchical,well-ordered process expressible as at least one network including aplurality of tasks to produce internal products corresponding tocomponents of at least one structural product produced thereby, at leastsome of the plurality of tasks having a precedence relationship wherebythe internal product produced by one task is an input to another task;defining, by the processing circuitry, a source lifecycle thread fromthe process-related information; and defining and matching, by theprocessing circuitry, a target lifecycle thread to the source lifecyclethread, the source lifecycle thread and target lifecycle thread eachbeing expressible as a sub-network of respectively source tasks andtarget tasks selected from the plurality of tasks, the target taskshaving at least a threshold similarity with the source tasks, and inputsto the target tasks having at least a threshold similarity with inputsto the source tasks, wherein the process-related information describesthe plurality of tasks by a plurality of attributes, and either or eachof defining the source lifecycle thread, or defining and matching thetarget lifecycle thread, includes at least: receiving user selection ofan attribute of the plurality of attributes as user-selected criteria;performing a cluster analysis according to the user-selected criteria toproduce a plurality of clusters of candidate tasks from the plurality oftasks, performing the cluster analysis including forming a grouping ofthe plurality of tasks according to the attribute, generating a distancemap for the grouping including only unique values of the attribute anddistances between the unique values, and organizing the tasks into theplurality of clusters of candidate tasks according to distance using thedistance map; and selecting tasks from the plurality of clusters ofcandidate tasks for the source tasks or target tasks.
 9. The method ofclaim 8, wherein receiving user selection of the attribute includesreceiving user selection of a plurality of attributes as theuser-selected criteria, wherein forming the grouping includes forming arespective plurality of groupings according to the plurality ofattributes, generating the distance map includes generating a distancemap for each grouping of the respective plurality of groupings, andorganizing the tasks includes organizing the tasks using the distancemap for each grouping, and wherein selecting the tasks includesselecting the tasks from a selected cluster of the plurality ofclusters.
 10. The method of claim 9, wherein receiving user selection ofthe plurality of attributes includes receiving user selection of theplurality of attributes and a weight for a particular attribute thereofas the user-selected criteria, and wherein performing the clusteranalysis further includes applying the weight to the distance map forthe grouping according to the particular attribute, and organizing thetasks includes organizing the tasks using the distance map for eachgrouping including the distance map with the weight so applied.
 11. Themethod of claim 10, wherein defining the source lifecycle thread ordefining the target lifecycle thread further includes receiving userselection of an adjusted weight for the particular attribute, andwherein performing the cluster analysis further includes applying theadjusted weight to the distance map for the grouping according to theparticular attribute, and reorganizing the tasks using the distance mapfor each grouping including the distance map with the adjusted weight soapplied.
 12. The method of claim 8, wherein each of defining the sourcelifecycle thread, and defining and matching the target lifecycle thread,includes receiving user selection of the attribute, performing thecluster analysis and selecting the tasks, and wherein in an instance inwhich the attribute received for both the source lifecycle thread andtarget lifecycle thread is the same, defining and matching the targetlifecycle thread includes organizing the tasks using the distance mapgenerated during the cluster analysis for the source lifecycle threadwithout again generating the distance map during the cluster analysisfor the target lifecycle thread.
 13. The method of claim 8, whereindefining and matching the target lifecycle thread includes at least:selecting a plurality of candidate target tasks from the plurality oftasks, the plurality of candidate target tasks being selected from theplurality of clusters of candidate tasks in an instance in whichdefining and matching the target lifecycle thread includes performingthe cluster analysis; matching a candidate target task to a particularsource task of the source lifecycle thread; and back-chaining throughthe source lifecycle thread including for each source task of at leastsome of the source tasks beginning with the particular source task:identifying a preceding source task that inputs to the source task; andmatching another candidate target task to the preceding source task, thetarget lifecycle thread being composed of the candidate target task andother candidate target task matched to the preceding source task foreach source task of the at least some of the source tasks.
 14. Themethod of claim 13, wherein in at least one instance in which thecandidate target task and other candidate target task are precedenceunrelated, back-chaining through the source lifecycle thread furtherincludes defining an input to the candidate target task from the othercandidate target task to thereby establish a precedence relationship.15. A computer-readable storage medium for processing data to establishand replicate lifecycle threads in the development of a structuralproduct, the computer-readable storage medium being non-transitory andhaving computer-readable program code portions stored therein that inresponse to execution by processing circuitry, cause an apparatus to atleast: access a memory storing process-related information describing atleast one hierarchical, well-ordered process expressible as at least onenetwork including a plurality of tasks to produce internal productscorresponding to components of at least one product produced thereby, atleast some of the plurality of tasks having a precedence relationshipwhereby the internal product produced by one task is an input to anothertask; define a source lifecycle thread from the process-relatedinformation; and define and match a target lifecycle thread to thesource lifecycle thread, the source lifecycle thread and targetlifecycle thread each being expressible as a sub-network of respectivelysource tasks and target tasks selected from the plurality of tasks, thetarget tasks having at least a threshold similarity with the sourcetasks, and inputs to the target tasks having at least a thresholdsimilarity with inputs to the source tasks, wherein the process-relatedinformation describes the plurality of tasks by a plurality ofattributes, and the apparatus being caused to either or each of definethe source lifecycle thread, or define and match the target lifecyclethread, includes being caused to at least: receive user selection of anattribute of the plurality of attributes as user-selected criteria;perform a cluster analysis according to the user-selected criteria toproduce a plurality of clusters of candidate tasks from the plurality oftasks, the apparatus being caused to perform the cluster analysisincluding being caused to form a grouping of the plurality of tasksaccording to the attribute, generate a distance map for the groupingincluding only unique values of the attribute and distances between theunique values, and organize the tasks into the plurality of clusters ofcandidate tasks according to distance using the distance map; and selecttasks from the plurality of clusters of candidate tasks for the sourcetasks or target tasks.
 16. The computer-readable storage medium of claim15, wherein the apparatus being caused to receive user selection of theattribute includes being caused to receive user selection of a pluralityof attributes as the user-selected criteria, wherein the apparatus beingcaused to form the grouping includes being caused to form a respectiveplurality of groupings according to the plurality of attributes, beingcaused to generate the distance map includes being caused to generate adistance map for each grouping of the respective plurality of groupings,and being caused to organize the tasks includes being caused to organizethe tasks using the distance map for each grouping, and wherein theapparatus being caused to select the tasks includes being caused toselect the tasks from a selected cluster of the plurality of clusters.17. The computer-readable storage medium of claim 16, wherein theapparatus being caused to receive user selection of the plurality ofattributes includes being caused to receive user selection of theplurality of attributes and a weight for a particular attribute thereofas the user-selected criteria, and wherein the apparatus being caused toperform the cluster analysis further includes being caused to apply theweight to the distance map for the grouping according to the particularattribute, and being caused to organize the tasks includes being causedto organize the tasks using the distance map for each grouping includingthe distance map with the weight so applied.
 18. The computer-readablestorage medium of claim 17, wherein the apparatus being caused to definethe source lifecycle thread or define the target lifecycle threadfurther includes being caused to receive user selection of an adjustedweight for the particular attribute, and wherein the apparatus beingcaused to perform the cluster analysis further includes being caused toapply the adjusted weight to the distance map for the grouping accordingto the particular attribute, and reorganize the tasks using the distancemap for each grouping including the distance map with the adjustedweight so applied.
 19. The computer-readable storage medium of claim 15,wherein the apparatus being caused to each of define the sourcelifecycle thread, and define and match the target lifecycle thread,includes being caused to receive user selection of the attribute,perform the cluster analysis and select the tasks, and wherein in aninstance in which the attribute received for both the source lifecyclethread and target lifecycle thread is the same, the apparatus beingcaused to define and match the target lifecycle thread includes beingcaused to organize the tasks using the distance map generated during thecluster analysis for the source lifecycle thread without againgenerating the distance map during the cluster analysis for the targetlifecycle thread.
 20. The computer-readable storage medium of claim 15,wherein the apparatus being caused to define and match the targetlifecycle thread includes being caused to at least: select a pluralityof candidate target tasks from the plurality of tasks, the plurality ofcandidate target tasks being selected from the plurality of clusters ofcandidate tasks in an instance in which the apparatus being caused todefine and match the target lifecycle thread includes being caused toperform the cluster analysis; match a candidate target task to aparticular source task of the source lifecycle thread; and back-chainthrough the source lifecycle thread including for each source task of atleast some of the source tasks beginning with the particular sourcetask, the apparatus being caused to at least: identify a precedingsource task that inputs to the source task; and match another candidatetarget task to the preceding source task, the target lifecycle threadbeing composed of the candidate target task and other candidate targettask matched to the preceding source task for each source task of the atleast some of the source tasks.
 21. The computer-readable storage mediumof claim 20, wherein in at least one instance in which the candidatetarget task and other candidate target task are precedence unrelated,the apparatus being caused to back-chain through the source lifecyclethread further includes being caused to define an input to the candidatetarget task from the other candidate target task to thereby establish aprecedence relationship.